arXiv:2509.21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models (LLMs) have demonstrated strong generative capabilities for this purpose, their applications in high-stakes domains like healthcare present unique challenges due to the risk of generating clinically incorrect or misleading information. In this work, we propose a novel query-based model collaboration framework that integrates expert-level domain knowledge to guide the aug
The increasing deployment of LLMs in high-stakes domains necessitates robust methods to ensure their reliability and accuracy, especially as concerns about generating incorrect information escalate.
This work addresses critical safety and trustworthiness issues in AI applications within sensitive sectors like healthcare, potentially accelerating the adoption of LLM-driven solutions through improved accuracy and reduced risk.
The proposed framework introduces a method for integrating expert domain knowledge into LLM-based data augmentation for clinical text, moving beyond purely generative approaches to build more reliable and contextually aware AI systems.
- · Healthcare AI developers
- · Medical research institutions
- · Patients receiving AI-assisted care
- · NLP researchers focused on medical applications
- · Providers of unvalidated LLM solutions
- · AI models lacking domain-specific safeguards
Improved clinical text augmentation leads to more robust and accurate AI models for healthcare applications.
Increased trust in AI systems within healthcare facilitates broader adoption and integration into clinical workflows.
The methodology could be generalized to other high-stakes domains requiring expert oversight for AI-driven data generation and model training.
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Read at arXiv cs.LG